Trainable method of parametric shape description
Image and Vision Computing - Special issue: BMVC 1991
Active shape models—their training and application
Computer Vision and Image Understanding
Medical Image Analysis: Progress over Two Decades and the Challenges Ahead
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multilinear Analysis of Image Ensembles: TensorFaces
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
A Statistical Model of Respiratory Motion and Deformation of the Liver
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Mood swings: expressive speech animation
ACM Transactions on Graphics (TOG)
Style translation for human motion
ACM SIGGRAPH 2005 Papers
Bilinear Sparse Coding for Invariant Vision
Neural Computation
Separating Style and Content with Bilinear Models
Neural Computation
Using Bilinear Models for View-invariant Action and Identity Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Illumination-robust face recognition using ridge regressive bilinear models
Pattern Recognition Letters
Journal of Cognitive Neuroscience
Shape-based myocardial contractility analysis using multivariate outlier detection
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Localized shape variations for classifying wall motion in echocardiograms
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Characterizing spatio-temporal patterns for disease discrimination in cardiac echo videos
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Gait style and gait content: bilinear models for gait recognition using gait re-sampling
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
State-Space Analysis of Cardiac Motion With Biomechanical Constraints
IEEE Transactions on Image Processing
Bilinear point distribution models for heart motion analysis
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Atlas construction and image analysis using statistical cardiac models
STACOM'10/CESC'10 Proceedings of the First international conference on Statistical atlases and computational models of the heart, and international conference on Cardiac electrophysiological simulation challenge
Conditional shape models for cardiac motion estimation
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Integrating volumetric biomedical data in the virtual physiological human
Proceedings of the first international workshop on Managing interoperability and complexity in health systems
Bilinear spatiotemporal basis models
ACM Transactions on Graphics (TOG)
A survey of shaped-based registration and segmentation techniques for cardiac images
Computer Vision and Image Understanding
| Hi-index | 0.00 |
In this work we describe the usage of bilinear statistical models as a means of factoring the shape variability into two components attributed to inter-subject variation and to the intrinsic dynamics of the human heart. We show that it is feasible to reconstruct the shape of the heart at discrete points in the cardiac cycle. Provided we are given a small number of shape instances representing the same heart at different points in the same cycle, we can use the bilinear model to establish this.Using a temporal and a spatial alignment step in the preprocessing of the shapes, around half of the reconstruction errors were on the order of the axial image resolution of 2 mm, and over 90% was within 3.5 mm. From this, we conclude that the dynamics were indeed separated from the inter-subject variability in our dataset.